Person Recognition with Modular Deep Neural Network Using the Iris Biometric Measure

  • Fernando Gaxiola
  • Patricia Melin
  • Fevrier ValdezEmail author
  • Juan Ramón Castro
Part of the Studies in Computational Intelligence book series (SCI, volume 749)


In this paper a modular deep neural network architecture are applied for recognize persons based on the iris biometric measurement of humans. The modular neural network consists of three modules, each module work with a deep neural network. This paper works with the human iris database improved with image preprocessing methods, these methods make a cut of the area of interest allowing remove the noise around the human iris. The input to the modular deep neural network is the preprocessed iris images and the output is the person identified. The “Gating Network” integrator is used for the integration of the modules for obtain the final results.


Deep neural networks Face recognition Biometric 


  1. 1.
    P. Birajadar, P. Shirvalkar, S. Gupta, V. Patidar, U. Sharma, A. Naik, V. Gadre, A novel iris recognition technique using monogenic wavelet phase encoding, in 2016 International Conference on Signal and Information Processing (IConSIP), pp. 1–6 (2016)Google Scholar
  2. 2.
    F.R.G. Cruz, C.C.Hortinela, B.E. Redosendo, B.K. Asuncion, C.J. Leoncio, N.B. Linsangan, W. Chung, Iris recognition using Daugman algorithm on Raspberry Pi, in 2016 IEEE Region 10 Conference (TENCON), pp. 2126–2129 (2016)Google Scholar
  3. 3.
    J. Daugman, Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int. J. Comput. Vis. 45(1), 25–38 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    D. Erhan, P.A. Manzagol, Y. Bengio, S. Bengio, P. Vincent, The difficulty of training deep architectures and the effect of unsupervised pre-training, in AISTATS’2009, pp. 153–160 (2009)Google Scholar
  5. 5.
    F. Gaxiola, P. Melin, M. Lopez, Modular neural networks for person recognition using the contour segmentation of the human iris biometric measurement. Stud. Comput. Intell. 312, 137–153 (2010)Google Scholar
  6. 6.
    G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)CrossRefGoogle Scholar
  7. 7.
    Q. Jiang, L. Cao, M. Cheng, C. Wang, J. Li, Deep neural networks-based vehicle detection in satellite images, in 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 184–187 (2015)Google Scholar
  8. 8.
    L. Flom, A. Safir, Iris recognition system. U.S. Patent 4,641,349 (1987)Google Scholar
  9. 9.
    H. Larochelle, Y. Bengio, J. Louradour, P. Lamblin, Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10, 1–40 (2009)zbMATHGoogle Scholar
  10. 10.
    D. Li, G. Hinton, B. Kingsbury, New types of deep neural network learning for speech recognition and related applications: an overview, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8599–8603 (2013)Google Scholar
  11. 11.
    L. Masek, P. Kovesi, MATLAB source code for a biometric identification system based on iris patterns. The School of Computer Science and Software Engineering the University of Western Australia (2003)Google Scholar
  12. 12.
    A. Muroó, J. Pospisil, The human iris structure and its usages. Physica 39, 89–95 (2000)Google Scholar
  13. 13.
    M. Risk, H. Farag, L. Said, Neural network classification for iris recognition using both particle swarm optimization and gravitational search algorithm, in 2016 World Symposium on Computer Applications & Research (WSCAR), pp. 12–17 (2016)Google Scholar
  14. 14.
    S.M. Rhee, B. Yoo, J.J. Han, W. Hwang, Deep neural network using color and synthesized three-dimensional shape for face recognition. J. Electron. Imaging, 26(2) (2017)Google Scholar
  15. 15.
    O. Sánchez, J. González, Access control based on iris recognition, Technological University Corporation of Bolívar, Faculty of Electrical Engineering, Electronics and Mechatronics, Cartagena de Indias, Colombia, pp. 1–137 (2003)Google Scholar
  16. 16.
    K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in Conference on ICLR 2015, pp. 1–13 (2015)Google Scholar
  17. 17.
    C. Tisse, L. Martin, L. Torres, M. Robert, Person identification technique using human iris recognition, in Canadian Image Processing and Pattern Recognition Society (CIPPRS) 15th International Conference on Vision Interface, pp. 294–299 (2002)Google Scholar
  18. 18.
    Z. Zhang, C. Xu, W. Feng, Road vehicle detection and classification based on deep neural network, in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Fernando Gaxiola
    • 1
  • Patricia Melin
    • 1
  • Fevrier Valdez
    • 1
    Email author
  • Juan Ramón Castro
    • 1
  1. 1.Tijuana Institute of Technology, Autonomous University of Baja CaliforniaTijuanaMexico

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